Student Name & ID: Omar elalem (8937288)

Importing some important libraries of Python for Machine Learning

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import plotly 
import plotly.express as px
plotly.offline.init_notebook_mode()
sns.set_style("whitegrid")

Info about dataset present in Seaborn Repository

arj = sns.get_dataset_names()
print(arj,end="")

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']

Now we are using one of the Seaborn Repository data i.e. "IRIS" dataset

from IPython.display import Image
Image(url='https://www.tensorflow.org/images/iris_three_species.jpg')

data = sns.load_dataset("iris")
data.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

--> head() function give top five data from IRIS dataset

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   sepal_length  150 non-null    float64
 1   sepal_width   150 non-null    float64
 2   petal_length  150 non-null    float64
 3   petal_width   150 non-null    float64
 4   species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB

--> info() function give the brief information about dataset

data["species"].value_counts()

species
setosa        50
versicolor    50
virginica     50
Name: count, dtype: int64

-->values_counts() function return a Series containing counts of unique values.

Let's visualize the IRIS data using Matplotlib

plt.figure(figsize=(10,8))
ax1 = data[data.species=='setosa'].plot.scatter(x='sepal_length', y='sepal_width', label='Setosa')
ax2 = data[data.species=='versicolor'].plot.scatter(x='sepal_length', y='sepal_width', color='red', label='Versicolor')
ax3 = data[data.species=='virginica'].plot.scatter(x='sepal_length', y='sepal_width', color='orange', label='Virginica')
ax1.set_xlabel("Sepal Length")
ax1.set_ylabel("Sepal Width")
ax2.set_xlabel("Sepal Length")
ax2.set_ylabel("Sepal Width")
ax3.set_xlabel("Sepal Length")
ax3.set_ylabel("Sepal Width")
ax1.set_title("Relationship of Sepal Length and Sepal Width on Setosa")
ax2.set_title("Relationship of Sepal Length and Sepal Width on Versicolor")
ax3.set_title("Relationship of Sepal Length and Sepal Width on Virginica")

plt.show()

<Figure size 1000x800 with 0 Axes>

data.plot(kind="density",figsize=(6.5,5))
plt.show()

Let's visualize the IRIS data using Seaborn

plt.figure(figsize=(10,10))
sns.boxplot(x="sepal_length",y= "sepal_width", hue= "species",palette=["r","g","b"],data= data)

<Axes: xlabel='sepal_length', ylabel='sepal_width'>

Let's visualize the IRIS data using Plotly

fig = px.scatter_3d(data,x= "sepal_length", y= "sepal_width",z = "petal_length",size= "petal_width", color="species")
fig.show()